"""
GARCH波动率交易策略
策略逻辑：
1. 使用GARCH模型预测波动率
2. 在低波动时建仓，高波动时减仓
3. 动态调整仓位大小
"""

import numpy as np
import pandas as pd
from arch import arch_model

class GARCHStrategy:
    def __init__(self, ticker, lookback=252):
        self.ticker = ticker
        self.lookback = lookback  # 使用1年数据
        self.model = None
        
    def fit_model(self, returns):
        """拟合GARCH(1,1)模型"""
        self.model = arch_model(returns, vol='Garch', p=1, q=1)
        self.results = self.model.fit(disp='off')
        
    def forecast_volatility(self, returns):
        """预测未来波动率"""
        if self.model is None:
            self.fit_model(returns)
            
        forecasts = self.results.forecast(horizon=5)
        return np.sqrt(forecasts.variance.values[-1][-1])
        
    def generate_signal(self, current_price, recent_returns):
        """生成交易信号"""
        if len(recent_returns) < self.lookback:
            return 0
            
        # 计算波动率预测
        vol = self.forecast_volatility(recent_returns)
        
        # 波动率策略逻辑
        if vol < 0.2:  # 低波动市场
            return 1.0  # 满仓
        elif vol > 0.4:  # 高波动市场
            return -1.0  # 空仓
        else:  # 中等波动
            return 0.5  # 半仓

if __name__ == '__main__':
    strategy = GARCHStrategy('SPY')
    
    # 模拟收益率数据
    np.random.seed(42)
    returns = np.random.randn(300) * 0.01
    
    # 生成信号
    signal = strategy.generate_signal(100, returns)
    print(f"波动率策略信号: {signal:.2f}")